The fact sources aren’t provided with ChatGPT is detrimental. We’re in the early stages where errors are not uncommon, sourcing the info used to generate the paraphrases with ChatGPT would help instill confidence in its responses. This is a primary reason I prefer using perplexity.ai over ChatGPT, plus there’s no need to create an account with perplexity as well!
While it does provide sources, it partially did not actually take information from them (the second source contradicts with the first source) or lost lots of context on the way, like the first source only talking about black ants, not all ants.
That’s not the point I’m trying to make here. I just didn’t want to dismiss your suggestion without even looking at it.
Because yeah, my understanding is that we’re not in “early stages”. We’re in the late stages of what these Large Language Models are capable of. And we’ll need significantly more advances in AI before they truly start to understand what they’re writing.
Because these LLMs are just chaining words together that seem to occur together in the wild. Some fine-tuning of datasets and training techniques may still be possible, but the underlying problem won’t go away.
You need to actually understand what these sources say to compare them and determine that they are conflicting and why. It’s also not enough to pick one of these sources and summarize it. As a human, it’s an essential part of research to read multiple sources to get a proper feel for the ‘correct’ answer and why there is not just one correct answer.
you could say human brains are also “just chaining words together that seem to occur together in the wild”. What is thinking if not bringing ideas (words) together that we’ve learned from our environment (the wild)?
The major difference I see, is that current AIs only provide narrow AI. They have very few sensors and are optimized for very few tasks.
Broad AI or human intelligence involves tons of sensors/senses which may not directly be involved in a given task, but still allow you to judge its success independently. We also need to perform many different tasks, some of which may be similar to a new task we need to tackle.
And humans spend several decades running around with those senses in different situations, performing different tasks, constantly evaluating their own success.
For example, writing a poem. ChatGPT et al can do that. But they can’t listen to someone reading their poem, to judge how the rhythm of the words activates their reward system for successful pattern predictions, like it does for humans.
They also don’t have complex associations with certain words. When we speak of a dreary sky, we associate coldness from sensing it with our skin, and we associate a certain melancholy, from our brain not producing the right hormones to keep us fully awake.
A narrow AI doesn’t have a multitude of sensors + training data for it, so it cannot have such impressions.
Google especially is working on multimodal models that do both language and image, audio, etc understanding in the same model. Their latest work, PaLM-E, demonstrates that learning in one domain (eg images) can indirectly benefit the model’s performance in other domains (eg text) without additional training in the other domain.
In your search result, click “view detailed” in the upper right, should be more what you’re looking for. I hope setting detailed view as default becomes an option.
The fact sources aren’t provided with ChatGPT is detrimental. We’re in the early stages where errors are not uncommon, sourcing the info used to generate the paraphrases with ChatGPT would help instill confidence in its responses. This is a primary reason I prefer using perplexity.ai over ChatGPT, plus there’s no need to create an account with perplexity as well!
I mean, I just looked at one example: https://www.perplexity.ai/?s=u&uuid=aed79362-b763-40a2-917a-995bf2952fd7
While it does provide sources, it partially did not actually take information from them (the second source contradicts with the first source) or lost lots of context on the way, like the first source only talking about black ants, not all ants.
That’s not the point I’m trying to make here. I just didn’t want to dismiss your suggestion without even looking at it.
Because yeah, my understanding is that we’re not in “early stages”. We’re in the late stages of what these Large Language Models are capable of. And we’ll need significantly more advances in AI before they truly start to understand what they’re writing.
Because these LLMs are just chaining words together that seem to occur together in the wild. Some fine-tuning of datasets and training techniques may still be possible, but the underlying problem won’t go away.
You need to actually understand what these sources say to compare them and determine that they are conflicting and why. It’s also not enough to pick one of these sources and summarize it. As a human, it’s an essential part of research to read multiple sources to get a proper feel for the ‘correct’ answer and why there is not just one correct answer.
you could say human brains are also “just chaining words together that seem to occur together in the wild”. What is thinking if not bringing ideas (words) together that we’ve learned from our environment (the wild)?
The major difference I see, is that current AIs only provide narrow AI. They have very few sensors and are optimized for very few tasks.
Broad AI or human intelligence involves tons of sensors/senses which may not directly be involved in a given task, but still allow you to judge its success independently. We also need to perform many different tasks, some of which may be similar to a new task we need to tackle.
And humans spend several decades running around with those senses in different situations, performing different tasks, constantly evaluating their own success.
For example, writing a poem. ChatGPT et al can do that. But they can’t listen to someone reading their poem, to judge how the rhythm of the words activates their reward system for successful pattern predictions, like it does for humans.
They also don’t have complex associations with certain words. When we speak of a dreary sky, we associate coldness from sensing it with our skin, and we associate a certain melancholy, from our brain not producing the right hormones to keep us fully awake.
A narrow AI doesn’t have a multitude of sensors + training data for it, so it cannot have such impressions.
Google especially is working on multimodal models that do both language and image, audio, etc understanding in the same model. Their latest work, PaLM-E, demonstrates that learning in one domain (eg images) can indirectly benefit the model’s performance in other domains (eg text) without additional training in the other domain.
In your search result, click “view detailed” in the upper right, should be more what you’re looking for. I hope setting detailed view as default becomes an option.